Index of papers in Proc. ACL 2012 that mention
  • co-occurrence
Bansal, Mohit and Klein, Dan
Abstract
Specifically, we exploit short-distance cues to hypernymy, semantic compatibility, and semantic context, as well as general lexical co-occurrence .
Introduction
For example, we can collect the co-occurrence statistics of an anaphor with various candidate antecedents to judge relative surface affinities (i.e., (Obama, president) versus (Jobs, president)).
Introduction
We can also count co-occurrence statistics of competing antecedents when placed in the context of an anaphoric pronoun (i.e., Obama ’s election campaign versus Jobs’ election campaign).
Introduction
We explore five major categories of semantically informative Web features, based on (1) general lexical affinities (via generic co-occurrence statistics), (2) lexical relations (via Hearst-style hypernymy patterns), (3) similarity of entity-based context (e.g., common values of y for
Semantics via Web Features
The first four types are most intuitive for mention pairs where both members are non-pronominal, but, aside from the general co-occurrence group, helped for all mention pair types.
Semantics via Web Features
3.1 General co-occurrence
Semantics via Web Features
These features capture co-occurrence statistics of the two headwords, i.e., how often hl and hg are seen adjacent or nearly adjacent on the Web.
co-occurrence is mentioned in 17 sentences in this paper.
Topics mentioned in this paper:
Bruni, Elia and Boleda, Gemma and Baroni, Marco and Tran, Nam Khanh
Distributional semantic models
In all cases, occurrence and co-occurrence statistics are extracted from the freely available ukWaC and Wackypedia corpora combined (size: 1.9B and 820M tokens, respectively).1 Moreover, in all models the raw co-occurrence counts are transformed into nonnegative Local Mutual Information (LMI) scores.2 Finally, in all models we harvest vector representations for the same words (lemmas), namely the top 20K most frequent nouns, 5K most frequent adjectives and 5K most frequent verbs in the combined corpora (for coherence with the vision-based models, that cannot exploit contextual information to distinguish nouns and adjectives, we merge nominal and adjectival usages of the color adjectives in the text-based models as well).
Distributional semantic models
Window2 records sentence-internal co-occurrence with the nearest 2 content words to the left and right of each target concept, a narrow context definition expected to capture taxonomic relations.
Distributional semantic models
We further introduce hybrid models that exploit the patterns of co-occurrence of words as tags of the same images.
Experiment 2
11We also experimented with a model based on direct co-occurrence of adjectives and nouns, obtaining promising results in a preliminary version of Exp.
Experiment 2
our results suggest that co-occurrence in an image label can be used as a surrogate of true visual information to some extent, but the behavior of hybrid models depends on ad-hoc aspects of the labeled dataset, and, from an empirical perspective, they are more limited than truly multimodal models, because they require large amounts of rich verbal picture descriptions to reach good coverage.
Introduction
Traditional semantic space models represent meaning on the basis of word co-occurrence statistics in large text corpora (Tumey and Pantel, 2010).
Introduction
We also show that “hybrid” models exploiting the patterns of co-occurrence of words as tags of the same images can be a powerful surrogate of visual information under certain circumstances.
Related work
Like us, Ozbal and colleagues use both a textual model and a visual model (as well as Google adjective-noun co-occurrence counts) to find the typical color of an object.
co-occurrence is mentioned in 11 sentences in this paper.
Topics mentioned in this paper:
Neubig, Graham and Watanabe, Taro and Mori, Shinsuke and Kawahara, Tatsuya
Experiments
In this section, we compare the translation accuracies for character-based translation using the phrasal ITG model with and without the proposed improvements of substring co-occurrence priors and lookahead parsing as described in Sections 4 and 5.2.
Experiments
Table 5: METEOR scores for alignment with and without lookahead and co-occurrence priors.
Introduction
This method is attractive, as it is theoretically able to handle all sparsity phenomena in a single unified framework, but has only been shown feasible between similar language pairs such as Spanish-Catalan (Vilar et al., 2007), Swedish-Norwegian (Tiedemann, 2009), and Thai-Lao (Somlertlamvanich et al., 2008), which have a strong co-occurrence between single characters.
Substring Prior Probabilities
In this section, we overview an existing method used to calculate these prior probabilities, and also propose a new way to calculate priors based on substring co-occurrence statistics.
Substring Prior Probabilities
5.2 Substring Co-occurrence Priors
Substring Prior Probabilities
Instead, we propose a method for using raw substring co-occurrence statistics to bias alignments towards substrings that often co-occur in the entire training corpus.
co-occurrence is mentioned in 13 sentences in this paper.
Topics mentioned in this paper:
Guo, Weiwei and Diab, Mona
Evaluation for SS
After the SS model learns the co-occurrence of words from WN definitions, in the testing phase, given an ON definition d, the SS algorithm needs to identify the equivalent WN definitions by computing the similarity values between all WN definitions and the ON definition d, then sorting the values in decreasing order.
Experiments and Results
For the Brown corpus, each sentence is treated as a document in order to create more coherent co-occurrence values.
Introduction
2. word co-occurrence information is not sufficiently exploited.
Limitations of Topic Models and LSA for Modeling Sentences
LSA and PLSNLDA work on a word-sentence co-occurrence matrix.
Limitations of Topic Models and LSA for Modeling Sentences
The yielded M X N co-occurrence matrix X comprises the TF-IDF values in each X ij cell, namely that TF-IDF value of word w, in sentence 83-.
co-occurrence is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Chen, David
Online Lexicon Learning Algorithm
10: for connected subgraph g of p; such that the size of g is less than or equal to m do 11: Increase the co-occurrence count of g and w by l 12: end for 13: end for
Online Lexicon Learning Algorithm
From the corresponding navigation plan, we find all connected subgraphs of size less than or equal to m. We then update the co-occurrence counts between all the n-grams w and all the connected subgraphs 9.
Online Lexicon Learning Algorithm
This allows us to determine if two graphs are identical in constant time and also lets us use a hash table to quickly update the co-occurrence and subgraph counts.
co-occurrence is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Kuznetsova, Polina and Ordonez, Vicente and Berg, Alexander and Berg, Tamara and Choi, Yejin
Surface Realization
NPMI(ngr) = (21) Where NPMI is the normalized point-wise mutual information.4 Co—occurrence Cohesion Score: To capture long-distance cohesion, we introduce a co-occurrence-based score, which measures order-preserved co-occurrence statistics between the head words hsij and hqu 5.
Surface Realization
co-occurrence cohesion is computed as:
Surface Realization
Final Cohesion Score: Finally, the pairwise phrase cohesion score 177;qu is a weighted sum of n-gram and co-occurrence cohesion scores: NGRAM CO a ° Fsiqu + fl ° Faiqu (23) a + fl
co-occurrence is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Mukherjee, Arjun and Liu, Bing
Experiments
Thus, having only one seed per seed set will result in sampling that single word whenever that seed set is chosen which will not have the effect of correlating seed words so as to pull other words based on co-occurrence with constrained seed words.
Proposed Seeded Models
The standard LDA and existing aspect and sentiment models (ASMs) are mostly governed by the phenomenon called “higher-order co-occurrence” (Heinrich, 2009), i.e., based on how often terms co-occur in different contexts].
Proposed Seeded Models
W1 co-occumng With W2 Wthh in turn co-occurs With W3 denotes a second-order co-occurrence between W1 and W3.
co-occurrence is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Xiao, Xinyan and Xiong, Deyi and Zhang, Min and Liu, Qun and Lin, Shouxun
Estimation
In the first step, we estimate the correspondence probability by the co-occurrence of the source-side and the target-side topic assignment of the word-aligned corpus.
Estimation
Thus, the co-occurrence of a source-side topic with index kf and a target-side
Estimation
We then compute the probability of P(z = kf|z 2 Ice) by normalizing the co-occurrence count.
co-occurrence is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: